Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

9 papers of 11,817Sort Recent · Most cited
  1. 2025
    CADE: Continual Weakly-supervised Video Anomaly Detection with EnsemblesSatoshi Hashimoto, Tatsuya Konishi, Tomoya Kaichi … Mori KurokawaWACV
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  2. 2025
    Learning After Model DeploymentDerda Kaymak, Gyuhak Kim, Tomoya Kaichi … Bing LiuEuropean Conference on Artificial Intelligence
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  3. 2023
    Parameter-Level Soft-Masking for Continual LearningTatsuya Konishi, M. Kurokawa, C. Ono … Bin LiuICML
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  4. 2023
    Learnability and Algorithm for Continual LearningGyuhak Kim, Changnan Xiao, Tatsuya Konishi, Bin LiuICML
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  5. 2023
    Open-World Continual Learning: Unifying Novelty Detection and Continual LearningGyuhak Kim, Changnan Xiao, Tatsuya Konishi … Bin LiuArtificial Intelligence
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  6. 2023
    Continual Pre-training of Language ModelsZixuan Ke, Yijia Shao, Haowei Lin … Bin LiuICLR
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  7. 2023
    Continual Learning of Language ModelsZixuan Ke, Yijia Shao, Haowei Lin … Bin LiuICLR
  8. 2022
    A Theoretical Study on Solving Continual LearningGyuhak Kim, Changnan Xiao, Tatsuya Konishi … Bing LiuNeurIPS
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  9. 2022
About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.